Deciphering the multi-target anticancer potential of β-sitosterol in breast cancer through integrated computational approaches

Therapeutic Potential of Icariin in Breast Cancer: An Integrated Network Pharmacology and Molecular Simulations Approach

Findings support the hypothesis that ICA may serve as a valuable natural compound for treating HER2‐driven breast cancer; it requires further experimental validation.

Integrated GC-MS Profiling and Multi-Scale Computational Analyses Identify Promising Phytochemicals Leads Targeting MAPK14 in Colorectal Cancer

Computational Investigation of Cinnamon Phytochemicals Targeting Key Cancer Signaling Pathways: Molecular Docking, ADMET and Molecular Dynamics Simulations Analysis

Computational predictions of potential interactions between selected cinnamon-derived phytochemicals and cancer-associated signaling proteins are provided and are consistent with the preliminary observation that the crude cinnamon extract exhibits antioxidant activity and cytotoxic effects in colorectal cancer cell lines.

Genistein as a Potential Breast Cancer Therapeutic: Computational Investigations Targeting EGFR and Oncogenic Pathways

Genistein is a promising multitarget EGFR‐modulating compound that warrants further experimental validation through in vitro and in vivo studies, according to an integrated computational approach.

Multi-Target Pharmacological Mechanisms of Cannabidiol in Breast, Colorectal, and Lung Cancer: An Integrated Network Pharmacology and Molecular Docking Study

Cannabidiol (CBD), the principal non-psychoactive phytocannabinoid of Cannabis sativa, exhibits diverse pharmacological activities through interactions with multiple molecular targets. Thus, breast, colorectal, and lung cancers arise from distinct molecular mechanisms. This study investigated the potential multi-target pharmacological mechanisms of CBD using an integrated approach combining network pharmacology and molecular docking. CBD-associated targets from three prediction platforms were intersected with disease-associated genes for each cancer type, yielding 143 overlapping targets that formed a significantly enriched protein–protein interaction network. Maximal Clique Centrality (MCC) analysis identified 10 hub proteins, including SRC, SIRT1, PTGS2 (COX-2), PPARG, NFKB1, MMP2, IGF1R, ESR2, ESR1, and EGFR, which represent key regulators of hormone signaling, inflammation, cell proliferation, and tumor progression. Molecular docking against these targets, benchmarked using each protein’s authentic co-crystallized ligand, predicted predominantly moderate binding affinities for CBD. Compared with the corresponding reference ligands, CBD generally exhibited lower predicted binding affinity, although comparable or slightly stronger scores were observed for PTGS2, ESR2, and EGFR. Independent validation using AutoDock Vina demonstrated overall agreement with the MOE docking results, supporting the robustness of the predicted binding profiles. Collectively, these findings suggest that CBD may exert its biological activity through coordinated modulation of multiple cancer-related signaling pathways rather than a single molecular target. By integrating pooled cancer-associated network pharmacology with co-crystallized ligand benchmarking, this study provides a computational framework for prioritizing biologically relevant CBD targets for future experimental validation. These findings should be regarded as hypothesis-generating rather than evidence of clinical efficacy.

Multi-tiered in silico evaluation identifies Sarsasapogenin as a promising ERα-targeted phytochemical against breast cancer.

Sarsasapogenin, a spirostanol sapogenin with reported activity against ERα-positive breast cancer cells, has no defined molecular target, and its metabolic fate has not been considered in computational studies of this compound class. Network pharmacology, biotransformation profiling, and multi-level molecular modeling were combined to address both questions. Of 108 targets shared between Sarsasapogenin and breast cancer, ERα gave the most favorable docking energy among ten hub proteins (-10.46 kcal/mol), against -8.73 kcal/mol for the reference modulator Bazedoxifene. BioTransformer predicted 16 metabolites, of which five phase-I derivatives retaining the spirostanol scaffold bound ERα within a narrow window (-10.01 to -10.30 kcal/mol). The representative metabolite BTM00010, a 6-hydroxylated derivative, retained affinity at -10.30 kcal/mol and formed a hydrogen bond to Val533 that is absent from the parent pose. Over 100 ns of simulation, all three complexes reached comparable plateaus in RMSD (0.20-0.30 nm) and radius of gyration (1.68-1.80 nm), and MMGBSA ranked them in the same order as docking (-47.30, -21.48, and -11.83 kcal/mol). Post-dynamics analysis showed lower collective-motion amplitude for the metabolite complex than for the parent, and a correlated-motion network closer to the parent than to the reference modulator. Predicted phase-I hydroxylation, therefore, does not abolish ERα engagement, which argues for evaluating biotransformation products alongside the parent compound in computational screening of plant sapogenins.